Simulation method and system for considering mulching factor in crop growth model

Abstract

The application provides a simulation method and system considering mulching factors in a crop growth model, relates to the technical field of mulching, and the application obtains data under different irrigation conditions of a research area; introduces a corresponding mulching module in the crop growth model, constructs a crop growth model under mulching, inputs the obtained crop growth data, soil water and heat data and meteorological data into the crop growth model under mulching, calibrates and verifies soil hydraulic parameters, thermal parameters and crop parameters in the model, takes actual meteorological conditions and soil properties of the research area as a benchmark, simulates various irrigation systems by using the verified crop growth model, and determines an irrigation regulation mode of a mulched farmland according to a simulation result. The application proposes a mulching effect introduction scheme suitable for crop models with great differences in principle structures, so that each model can more comprehensively and accurately simulate soil water and heat migration and crop growth processes under mulching conditions.

Description

Technical Field

[0001] This application relates to the field of plastic film mulching technology, and in particular to a simulation method and system for considering plastic film mulching factors in crop growth models. Background Technology

[0002] Global water scarcity poses a serious threat to food security. Mulching technology, due to its significant effects on warming, moisture retention, and yield increase, has been widely used globally. Crop growth models rarely consider the impact of mulching; only the AquaCrop model simply considers the effects of mulching material and mulch ratio on soil evaporation. However, besides reducing soil evaporation, mulching also impedes rainfall, improves soil temperature, and changes in soil hydrothermal conditions under mulching can affect crop growth. The current lack of crop models that consider mulching factors limits their application. Therefore, it is necessary to comprehensively consider the effects of mulching and establish crop growth models applicable to simulating mulched farmland. However, different types of crop growth models differ significantly in their principles of soil hydrothermal migration and crop growth processes, as well as in the specific schemes for considering mulching factors.

[0003] Currently, in simulating soil hydrothermal dynamics and crop growth processes under plastic film mulching conditions, a common practice is to use existing models that do not consider the mulching factor and indirectly distinguish between mulched and unmulched treatments by adjusting model parameters. Furthermore, when using the Hydrus model for simulation, the upper boundary condition for moisture in the mulched area is usually set to a zero flux boundary to reflect the barrier effect of the mulch on surface water exchange.

[0004] Due to the lack of models that take into account the factors of plastic film mulching, researchers have directly applied crop growth models to plastic film-covered farmland. Although this approach can achieve a good fit in specific years and locations, the calibrated parameters do not reflect the actual physical effects of the plastic film, which will bring great uncertainty to the model predictions.

[0005] The Hydrus model simplifies the upper boundary condition for soil moisture movement under plastic film mulch to a zero-flux boundary, neglecting preferential flow at film pores and film breaks. Furthermore, the zero-flux boundary only addresses moisture movement and does not consider the impact of plastic film mulch on soil heat flux. Additionally, the Hydrus model lacks a crop growth module and cannot characterize the dynamic growth process of crops under plastic film mulch. Summary of the Invention

[0006] This application aims to at least partially address one of the technical problems in the related art.

[0007] This application proposes a simulation method that considers plastic film mulch in crop growth models. It proposes plastic film mulch effect introduction schemes applicable to crop models with significantly different underlying structures, enabling each model to more comprehensively and accurately simulate soil water and heat transport and crop growth processes under mulch conditions. The main problems addressed by this method include: considering the impact of plastic film mulch on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages in crop growth models; and developing specific introduction schemes for plastic film mulch factors tailored to the different underlying structures of crop models.

[0008] In another aspect, this application proposes a simulation system that considers the mulch film covering factor in a crop growth model.

[0009] To achieve the above objectives, the first aspect of this application proposes a simulation method that considers the mulch film covering factor in a crop growth model, comprising: Acquire crop growth data, soil hydrothermal data, and meteorological data under different irrigation conditions in the study area; Based on the effects of plastic film mulching on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, a corresponding plastic film mulching module is introduced into the crop growth model to construct a crop growth model under plastic film mulching. The acquired crop growth data, soil water and heat data, and meteorological data are input into the crop growth model under the mulch film. The soil hydraulic parameters, thermal parameters, and crop parameters in the model are calibrated and verified to ensure that the simulated values ​​match the measured values. Based on the actual meteorological conditions and soil properties of the study area, a variety of irrigation regimes were simulated using a validated crop growth model, and the irrigation regulation mode for farmland covered with plastic film was determined based on the simulation results.

[0010] In one embodiment of this application, the step of introducing a corresponding mulch film module into the crop growth model based on the effects of mulch film on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, and constructing a crop growth model under mulch film coverage, includes: Based on the interception effect of mulch on rainfall, the film interception process is quantified in a crop growth model to calculate the actual amount of rainfall infiltrating into the soil. Based on the inhibitory effect of plastic film mulching on soil evaporation, a soil evaporation reduction factor was introduced into the crop growth model to calculate the amount of soil evaporation under mulching conditions. Based on the warming effect of plastic film mulching on soil temperature, plastic film parameters related to soil heat are introduced into the crop growth model to calculate the soil surface temperature under mulching conditions. Based on the promoting effect of soil warming under plastic film mulching on crop growth and development stages, the relationship between crop development stages and soil temperature is established in a crop growth model to calculate the crop growth and development stages under mulching conditions.

[0011] In one embodiment of this application, the quantification film retention process for calculating the actual amount of rainfall infiltrating into the soil includes: Canopy interception is calculated based on leaf area index and soil cover ratio; The film retention amount is calculated based on the difference between the total rainfall and the canopy retention amount; The actual amount of rainfall that infiltrates into the soil is calculated based on total rainfall, canopy interception, and film interception.

[0012] In one embodiment of this application, the introduction of a soil evaporation reduction factor to calculate soil evaporation under mulching conditions includes: calculating soil evaporation under mulching conditions based on the mulching ratio and mulching resistance.

[0013] In one embodiment of this application, the step of introducing soil heat-related mulch film parameters to calculate the soil surface temperature under mulch film conditions includes: calculating the soil surface temperature under mulch film conditions by considering the influence of film thickness and film thermal conductivity on the heat transfer between the film and the soil surface; or calculating the soil temperature under mulch film conditions by considering the influence of mulch film control factors, including mulch film optical property parameters, mulch film ratio, and film-soil spacing on the surface energy balance budget and distribution process.

[0014] In one embodiment of this application, establishing the relationship between crop development stages and soil temperature to calculate crop growth and development stages under mulching conditions includes: The crop growth and development stages under mulching conditions are calculated based on the soil temperature at a depth of 5cm, the baseline temperature, and the temperature required to complete the nutrient or reproductive stages.

[0015] In one embodiment of this application, the calibration and verification of the soil hydraulic parameters, thermal parameters, and crop parameters in the model includes: Based on the obtained crop growth data, soil water and heat data, and meteorological data of the study area, the soil hydraulic parameters were first calibrated based on the dynamic changes in soil moisture in the soil profile, then the soil thermal parameters were calibrated based on the temperature changes in the soil profile, and finally the crop parameters were calibrated based on the crop leaf area index, dry matter accumulation, and yield. The model parameters were then validated using the remaining experimental data.

[0016] In one embodiment of this application, the simulation of multiple irrigation regimes includes: setting different irrigation intervals and irrigation volume scenarios, and using a validated crop growth model to simulate crop growth and soil hydrothermal conditions under each scenario.

[0017] In one embodiment of this application, determining the irrigation control mode for farmland covered with plastic film based on simulation results includes: using the entropy weight method to comprehensively evaluate different irrigation systems, and determining the optimal irrigation system based on the comprehensive score.

[0018] Furthermore, to achieve the above embodiments, this application proposes a simulation system that considers the mulch film covering factor in a crop growth model, comprising: The raw data acquisition module is used to acquire crop growth data, soil hydrothermal data, and meteorological data under different irrigation conditions in the study area; The growth model construction module is used to introduce the corresponding mulch film module into the crop growth model based on the effects of mulch film on rainfall interception, soil evaporation, soil temperature and crop growth and development stages, and to construct the crop growth model under mulch film. The parameter calibration and verification module is used to input the acquired crop growth data, soil water and heat data and meteorological data into the crop growth model under the mulch film, and to calibrate and verify the soil hydraulic parameters, thermal parameters and crop parameters in the model so that the simulated values ​​match the measured values. The simulation output module is used to simulate various irrigation regimes based on the actual meteorological conditions and soil properties of the study area, using a validated crop growth model, and to determine the irrigation regulation mode for farmland covered with plastic film based on the simulation results.

[0019] The methods and systems of this application can accurately simulate the crop growth process and soil hydrothermal dynamics under plastic film mulching, providing a scientific tool for the subsequent application of crop models in plastic film-mulched farmland.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a simulation method for considering plastic film mulching factors in a crop growth model, provided as an embodiment of this application; Figure 2 This is a structural diagram of the AquaCrop model in Example 2; Figure 3 This is a structural diagram of the SWAP model in Example 3; Figure 4 This is a structural diagram of the CropSPAC model in Example 4; Figure 5This is a comparison chart of the measured and simulated values ​​of soil water storage, maize water consumption, soil temperature, canopy cover, aboveground dry matter accumulation, and yield in 2017, 2018, and 2019 for Examples 2, 3, and 4. Figure 6 The graph shows the measured and simulated values ​​of soil water storage, canopy coverage, aboveground dry matter accumulation, water consumption, and yield under different irrigation treatments (WF, WM, WL) in 2019 for Examples 2, 3, and 4, as well as the comparison between the measured and simulated values ​​of soil temperature under the WF treatment. Figure 7 Model evaluation diagrams for soil water storage, canopy cover, aboveground dry matter accumulation, water consumption and yield under WF, WM and WL treatments in 2019; Figure 8 The graphs show the yield and water productivity under different irrigation intervals and irrigation amounts in Examples 2, 3, and 4. Figure 9 This is a structural diagram of a simulation system that considers the mulch film covering factor in a crop growth model, as provided in an embodiment of this application. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] The following describes, with reference to the accompanying drawings, a simulation method and system for considering mulch film covering factors in a crop growth model, according to embodiments of this application.

[0025] Example 1 like Figure 1 As shown, this simulation method that considers mulch film occupancy in a crop growth model specifically includes the following steps: S1, to obtain crop growth data, soil hydrothermal data and meteorological data under different irrigation conditions in the study area.

[0026] Specifically, acquiring observational data on crop growth, soil hydrothermal dynamics, and meteorological elements under different irrigation conditions in the study area is fundamental to constructing and calibrating crop growth models under plastic film mulching. This step aims to collect multi-source datasets that comprehensively reflect the ecological processes of mulched farmland through systematic field experiments or locational observations. These datasets include: indicators characterizing crop growth and development processes and yield formation, such as leaf area index, canopy coverage, plant height, aboveground dry matter accumulation, and final yield; indicators reflecting soil moisture and heat conditions, such as volumetric water content and soil temperature at different soil depths; and meteorological elements driving model operation, including rainfall, air temperature, radiation, and wind speed. The acquisition of these data needs to cover different irrigation treatment levels to capture the impact of changes in water conditions on the crop-soil system and ensure the data's temporal continuity and spatial representativeness, thereby providing reliable experimental evidence for the subsequent calibration and validation of soil hydraulic parameters, thermal parameters, and crop parameters in the model.

[0027] As one implementation method, multiple drip irrigation gradient treatments can be set up in the mulched corn field, soil moisture can be monitored in layers using a time domain reflectometer, soil temperature can be automatically recorded by buried temperature sensors, crop morphology indicators and biomass can be measured regularly, and water consumption can be calculated using the water balance method. At the same time, the daily meteorological data of the study area can be recorded synchronously by a meteorological station.

[0028] This step, by acquiring multi-factor and multi-process measured data, provides the necessary data support for the construction and parameter calibration of the crop growth model under plastic film mulch, ensuring that the model can truly reflect the water and heat transport and crop growth characteristics of the mulched farmland, thereby improving the reliability of the simulation results and the accuracy of the prediction.

[0029] S2. Based on the impact of plastic film mulching on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, a corresponding plastic film mulching module is introduced into the crop growth model to construct a crop growth model under plastic film mulching.

[0030] Specifically, based on the effects of plastic film mulching on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, a corresponding plastic film mulching module is introduced into the crop growth model to construct a crop growth model under plastic film mulching. Specifically, the impact of plastic film mulching on soil moisture movement is achieved by altering the upper boundary conditions of soil moisture movement, which involve rainfall interception and soil evaporation processes; the impact of plastic film mulching on soil temperature is achieved by altering the upper boundary conditions of soil heat migration, which involve soil surface temperature or soil heat flux; and the impact of plastic film mulching on crop growth is achieved by promoting crop growth and development stages through soil warming under plastic film mulching. Based on these three effects, a corresponding plastic film mulching module is introduced into the crop growth model to construct a simulation model that comprehensively reflects the impact of plastic film mulching on soil water and heat transport and crop growth processes.

[0031] As one implementation method, for models where soil moisture movement is characterized by the farmland water balance equation and lacks a soil thermal module, factors such as canopy interception, film interception, soil evaporation reduction coefficient, and soil temperature compensation coefficient based on effective accumulated temperature can be introduced. For models where the upper boundary of soil moisture movement is a flux boundary and the upper boundary of soil temperature is a first-type boundary, the relationship between mulch ratio and soil evaporation reduction rate, the calculation method of soil surface temperature under mulch conditions, and crop development stage simulation factors based on soil temperature can be introduced. For models based on the principle of water and heat transfer in the soil-vegetation-atmosphere continuum, radiation distribution in energy balance, mulch resistance in latent heat and sensible heat flux, and daily growth degree calculation factors based on soil temperature can be introduced. These introduction methods all focus on the impact of mulch on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, ensuring that crop growth models with different principle structures can reasonably and accurately simulate key processes under mulch conditions within their own framework.

[0032] This step comprehensively considers the multiple effects of mulch on soil moisture, soil temperature, and crop growth and development, and directly introduces the corresponding mulch module based on physical mechanisms. It breaks through the single and incomplete consideration of mulch effects in existing models, significantly improves the realism and prediction accuracy of the model in simulating soil water and heat transport and crop growth under mulch conditions, and provides a universal framework for introducing mulch effects for different crop growth models.

[0033] S3. Input the acquired crop growth data, soil water and heat data, and meteorological data into the crop growth model under the mulch film, and calibrate and verify the soil hydraulic parameters, thermal parameters, and crop parameters in the model to make the simulated values ​​match the measured values.

[0034] Specifically, after inputting the acquired crop growth data, soil hydrothermal data, and meteorological data into the constructed crop growth model under plastic film mulch, the soil hydraulic parameters, thermal parameters, and crop parameters in the model need to be calibrated and validated to ensure that the model's simulated outputs of soil moisture dynamics, soil temperature changes, crop growth indicators, yield, and water consumption are consistent with the measured data. The calibration process is essentially a reverse solution process that optimizes or adjusts the model's parameter space based on measured data. By constructing a difference measurement function between simulated and measured values ​​and using iterative approximation or optimization algorithms to minimize this difference, the optimal parameter combination reflecting the actual conditions of the study area is determined. This process can be carried out in stages and with different objectives, depending on the model structure and parameter sensitivity. For example, the hydraulic parameters related to soil moisture movement can be calibrated first, followed by the thermal parameters related to soil heat transfer, and finally the crop parameters related to crop growth and development, to ensure the coordination and rationality of the parameters in each module in terms of physical mechanisms. During the calibration process, statistical indicators such as normalized root mean square error and coefficient of determination can be used to quantitatively evaluate the simulation effect and determine whether the parameter values ​​meet the accuracy requirements. As one implementation method, in specific applications, a trial-and-error approach can be used to gradually narrow down the parameter value range, and calibration can be performed sequentially according to soil moisture dynamics, soil temperature changes, crop leaf area index, dry matter accumulation, and yield. The model performance is evaluated using the aforementioned statistical indicators until the deviation between the simulated and measured values ​​is within an acceptable range.

[0035] This calibration step enables crop growth models under plastic film mulch to obtain parameter configurations that closely match measured data in specific study areas. This significantly improves the model's simulation accuracy and prediction reliability of soil water and heat transport and crop growth processes in mulched farmland, providing a scientific basis for subsequent applications such as irrigation system optimization.

[0036] S4, based on the actual meteorological conditions and soil properties of the study area, uses the validated crop growth model to simulate various irrigation systems, and determines the irrigation regulation mode for farmland covered with plastic film based on the simulation results.

[0037] Specifically, based on fundamental parameters such as actual meteorological conditions and soil properties in the study area, a validated crop growth model was used to simulate various irrigation regimes. This process aimed to generate a series of different irrigation scenarios by changing key parameters in the irrigation regime, such as irrigation intervals and single irrigation amounts. By running the model, simulation results of key indicators such as crop growth status, soil hydrothermal dynamics, yield, and water use efficiency could be obtained for each scenario. Based on these simulation results, the comprehensive performance of different irrigation regimes could be evaluated, thereby determining the irrigation control mode that can achieve the goals of water conservation and yield increase. As one implementation method, multiple different irrigation interval and irrigation amount scenarios could be set, and comprehensive evaluation methods such as the entropy weight method, combined with indicators such as yield and water productivity, could be used to quantitatively score different irrigation regimes, thereby selecting the optimal irrigation control mode.

[0038] This step, through systematic simulation and evaluation of various irrigation regimes, can scientifically determine the optimal irrigation strategy for mulched farmland, thereby effectively improving water resource utilization efficiency and ensuring crop yield, and providing reliable technical support for precision irrigation management of mulched farmland.

[0039] Example 2 like Figure 2 The diagram shown is a structural diagram of the AquaCrop model in this embodiment. This embodiment proposes a simulation method that considers the mulch film covering factor in the crop growth model, which may further include the following steps: Step 1: Conduct experiments to obtain crop growth, yield, soil temperature, and soil moisture content before and after irrigation and rainfall under different irrigation conditions, and record meteorological data of the study area; Step 2: Based on the mulch film effect, a mulch film module is introduced into the crop model, specifically as follows: ① The impact of mulch film on soil moisture is mainly achieved by affecting the upper boundary conditions of soil moisture movement (such as rainfall interception and soil evaporation); ② The impact of mulch film on soil temperature is mainly achieved by affecting the upper boundary conditions of soil heat migration (soil surface temperature or soil heat flux); ③ The impact of mulch film on crop growth is mainly achieved by promoting crop growth and development stages through soil warming under mulch film. A crop growth model under mulch film is constructed based on these three processes.

[0040] Step 3: Input the crop growth, soil water and heat data, and meteorological data obtained in Step 1 into the crop model in Step 2, and calibrate and verify the soil hydraulic parameters, thermal parameters, and crop parameters of the crop model to ensure that the simulated soil moisture, soil temperature, crop growth indicators, yield, and water consumption match the measured values.

[0041] Step 4: Based on the actual meteorological conditions and soil properties of the study area, the validated crop model is used to simulate various irrigation systems. Based on the simulation results, the irrigation regulation mode for water saving and yield increase of farmland covered by plastic film in the study area is derived.

[0042] In one embodiment, the specific process is as follows: Step 1: In this embodiment, a three-year field experiment was conducted on cornfields covered with plastic film in the Shiyang River Basin of Northwest China, an arid region. The experimental periods were April 24, 2017 - September 8, 2017; April 20, 2018 - September 10, 2018; and April 18, 2019 - September 15, 2019. This embodiment included three drip irrigation treatments: WF, WM (70% WF), and WL (40% WF), with three replicates for each treatment. During the 2017 experiment, irrigation was conducted at 37, 49, 57, 67, 77, 83, 87, 107, and 127 days post-sowing (DAS), with corresponding irrigation amounts of 15.0, 7.6, 39.7, 42.7, 42.1, 25.6, 30.0, 42.0, and 61.3 mm for the WF treatment, respectively. During the 2018 experiment, irrigation was conducted at 43, 53, 62, 74, 84, 94, 104, and 123 days post-sowing (DAS), with corresponding irrigation amounts of 14.5, 22.2, 40.2, 40.5, 37.9, 40.6, 50.2, and 32.8 mm for the WF treatment, respectively. During the 2019 experiment, irrigation was conducted at 45, 55, 65, 75, 85, 94, 105, 115, and 127 days post-sowing (DAS), with corresponding irrigation amounts of 14.5, 22.2, 40.2, 40.5, 37.9, 40.6, 50.2, and 32.8 mm for the WF treatment, respectively. Irrigation was carried out on day d, with irrigation amounts corresponding to the WF treatments being 14.8, 31.4, 35.0, 25.0, 24.0, 40.5, 43.1, 33.7, and 41.3 mm, respectively. In this embodiment, all experimental plots were covered with a transparent plastic film 85 cm wide and 0.004 cm thick, with a 5 cm overlap between films, and then covered with soil. Fertilization was carried out according to local experience, i.e., 375 kg / hm² was applied before corn sowing. 2 diammonium phosphate and 375 kg / hm 2 Nitrogen, phosphorus, and potassium compound fertilizer was applied as base fertilizer, in three applications at a rate of 450 kg / hm² during the jointing stage. 2 Urea.

[0043] In this embodiment, soil moisture was monitored using a TRIME-TDR time-domain reflectometry system to measure the volumetric water content of the 0-100cm soil layer. Monitoring periods in 2017 were DAS = 14, 28, 35, 40, 45, 49, 51, 56, 60, 67, 69, 72, 76, 80, 88, 93, 97, 101, 106, 108, 114, 120, 126, 19, 135, and 138 days. Monitoring periods in 2018 were DAS = 7, 12, 17, 19, 26, 33, 40, 42, 44, 47, 52, 55, 61, 66, 68, 72, 77, 80, 83, 87, 90, 93, 98, 104, 108, 110, 116, 123, 126, 129, and 139 days. The monitoring periods in 2019 were DAS=3, 6, 12, 23, 27, 33, 40, 45, 47, 53, 57, 60, 68, 72, 76, 80, 84, 87, 93, 95, 100, 104, 107, 110, 113, 116, 120, 126, 130, 135, 144, and 150 days. Soil temperature was monitored using an HZ-J1 soil temperature sensor, with probes installed in the 10, 20, 40, 80, and 120 cm soil layers of the W1 treatment plot. The instrument automatically recorded the soil temperature every 30 minutes. Using a measuring tape with an accuracy of 0.01 m, plant height and leaf length and width were measured every 7-10 days to calculate leaf area index (DAS) and canopy coverage. The monitoring periods were as follows: 33, 40, 47, 54, 61, 68, 75, 82, 89, 96, 103, 110, and 122 days in 2017; 33, 40, 54, 61, 68, 76, 83, 90, 97, 107, 116, and 128 days in 2018; and 38, 46, 53, 61, 69, 74, 81, 92, 105, 117, and 128 days in 2019. Three representative maize plants were randomly selected from each plot. The plants were cut at the base of the stem to obtain intact above-ground plants. Leaves, stems, and fruits were separated and bagged. The plants were first blanched at 105 ℃ for 30 minutes, then dried at 85 ℃ to constant weight. Above-ground dry matter accumulation (DAS) was calculated based on this. Monitoring periods were: 44, 71, 93, 108, 128, and 137 days in 2017; 41, 57, 77, 97, 128, and 138 days in 2018; and 43, 59, 74, 89, 104, 124, and 149 days in 2019. After maize maturity, a row of maize was randomly selected from each plot, and 10 plants were selected consecutively as a group. Three replicates were performed to determine yield. The total water consumption for the entire growth cycle was calculated using the water balance method.

[0044] Step 2: In this embodiment, the specific consideration scheme for the mulch film covering factor in the crop model is as follows: For models where soil moisture movement is characterized by the farmland water balance equation and lacks a soil thermal module, the AquaCrop model is used as an example. Canopy Retention ΔP c The calculation method is as follows:

[0045]

[0046] In the formula, a This is an empirical coefficient, typically taken as 3.0 (mm d). –1 ); b This refers to the proportion of soil cover. P Total rainfall (mm); LAI is leaf area index (cm²). 2 cm –2 ); k e The extinction coefficient is denoted as .

[0047] Based on experience, in flat, plastic film-covered farmland, water can flow in all directions, and approximately 80% of rainfall will infiltrate into the soil through the seedling holes. Therefore, in this application, film interception is considered to be 20% of the effective rainfall. Film interception amount ΔP m The calculation method is as follows:

[0048] Actual rainfall infiltrating into the soil P The calculation method for 1 is as follows:

[0049] Soil evaporation is mainly affected by the evaporation reduction factor. K r Soil evaporation coefficient K e Compared to coating δ m The impact is calculated as follows:

[0050] Based on the principle of effective accumulated temperature effect, the effective accumulated temperature required for a crop to complete a certain growth stage is:

[0051] In the formula, T cum Effective accumulated temperature (°C); T The average daily air temperature or ground temperature (°C); T b The baseline temperature (°C) for the crop growth stage.

[0052] Under plastic film mulching, the accumulated ground temperature compensates for the insufficient effective accumulated air temperature over the same period, with a compensation coefficient of [missing value]. K for:

[0053] In the formula, K This is the thermal compensation coefficient for film coating; T cum–a–M1 The effective gas accumulation temperature (°C) under the membrane condition; T cum–a–M0 The effective gas accumulation temperature (°C) under uncoated conditions; T cum–s–M1 The effective accumulated temperature (°C) at a soil depth of 5 cm under the membrane covering condition; T cum–s–M0–M1 The effective accumulated temperature (°C) at a soil depth of 5 cm under conditions of simultaneous mulching and non-mulching.

[0054] The compensation value of soil temperature increase to air temperature under plastic film mulching conditions ΔT for:

[0055] In the formula, ΔT The compensation value (°C) for the increase in ground temperature under mulching conditions to the increase in air temperature. T s–M1 The average daily ground temperature (°C) at a soil depth of 5 cm under the film covering condition. T s–M0 The average daily ground temperature (°C) at a soil depth of 5 cm without covering. T a–M0 The average daily temperature (°C) is the temperature under the condition of no film covering.

[0056] The calculation method for the input parameters of air temperature under plastic film mulching conditions is as follows:

[0057] In the formula, T a–M1 The average daily temperature (°C) under the film covering condition.

[0058] Step 3: Input the crop growth, soil hydrothermal data, and meteorological data obtained in this embodiment into the AquaCrop crop model to calibrate and validate the soil moisture and crop parameters in the model, using a trial-and-error method to narrow down the parameter range. Experimental data from 2017 were used to calibrate the model parameters, while experimental data from 2018 and 2019 were used to validate them. During the calibration process, soil hydraulic parameters were first calibrated based on the dynamic changes in soil moisture in the soil profile, and then crop parameters were calibrated based on the leaf area index, dry matter accumulation, and final yield of maize. The normalized root mean square error (NRMSE) and coefficient of determination (R²) were used to calibrate the parameters. 2 To evaluate model performance.

[0059] Step 4: To study soil hydrothermal conditions and crop growth under different irrigation regimes, this embodiment sets different irrigation intervals (with the same total irrigation amount) and irrigation volumes. A total of seven irrigation interval scenarios were set: 4 days, 6 days, 8 days, 10 days, 12 days, 14 days, and 16 days; and ten irrigation volume scenarios: 30% WF, 40% WF, 50% WF, 60% WF, 70% WF, 80% WF, 90% WF, 100% WF, 110% WF, and 120% WF. The basic data are based on measured data from the 2017 WF treatment, aiming to explore water-saving and yield-increasing irrigation regimes under mulching conditions.

[0060] The entropy weight method was used to comprehensively evaluate different irrigation systems, with maize yield and water productivity as evaluation indicators. Due to the differences in dimensions and orders of magnitude between maize yield and water productivity, the raw data were standardized, and the indicator weights (p) were calculated. Then, the information entropy (…) was calculated. e ) and information entropy redundancy ( d To determine the weight of each indicator ( w Finally, calculate the overall score (). s ), used to evaluate the impact of the results.

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] In the formula, r ijStandardized values ​​for the data; x ij For the first i The original value of the j-th evaluation indicator among the evaluation objects; p ij The proportion of the indicator; m For sample size; e j For the first j Information entropy of each evaluation indicator; n The number of evaluation indicators; d j The information entropy redundancy of the j-th evaluation index; w j For the first j The weight of each evaluation indicator; s i For the first i The overall score of each evaluation subject.

[0068] Example 3 like Figure 3 The diagram shown is a SWAP model structure diagram of this embodiment. The specific solution of this embodiment is as follows: Step 1: Verify that the methods for obtaining the crop data, soil data, meteorological data and other indicators required for the crop model in this embodiment are the same as those in Embodiment 2, and will not be repeated here.

[0069] Step 2: In this embodiment, the method for considering the mulch film covering factor in the crop model is as follows: For models where the upper boundary condition for soil moisture movement is a type II boundary (i.e., a flux boundary determined by rainfall, irrigation, and evaporation), and the upper boundary condition for soil temperature is a type I boundary (i.e., specifying soil surface temperature), taking the SWAP model as an example... Canopy Retention ΔP c The calculation of rainfall interception is the same as that in the AquaCrop model in Example 2, that is:

[0070]

[0071]

[0072] Actual rainfall infiltrating into the soil P The calculation method for 1 is as follows:

[0073] Soil evaporation under mulching conditions ( E film The calculation method for ) is as follows:

[0074] In the formula, E pfilm Potential evapotranspiration (mmd) –1 ); E maxfilm Maximum evaporation rate (mm d) –1 ); ∑ E afilm It is the cumulative actual evaporation (mm).

[0075]

[0076] In the formula, C film The soil evaporation reduction rate under plastic film mulching conditions; Δ v The slope of the vapor pressure curve (kPa℃) –1 ); λ w Latent heat of vaporization (J kg) –1 ); R n Net radiative flux at the canopy surface (J m) –2 d –1 ); G Soil heat flux (J m – 2 d –1 ); p 1 represents unit conversion (86400 sd) –1 ); ρ air air density (kg m) –3 ); C air The heat capacity of moist air (J kg) –1 ℃ –1 ); e sat The saturated vapor pressure is (kPa). e a This is the actual vapor pressure (kPa); r air，can aerodynamic drag of uniform crop (sm –1 ); γ air Humidity constant (kPa℃) –1 ); r air，soil Aerodynamic drag on the soil surface (sm –1 ); r soil Soil resistance (Sm) for moist soil –1 On a daily scale, GIt is disregarded.

[0077]

[0078] In the formula, k 1 / 2 The average hydraulic conductivity (mm d) between the soil surface and the adjacent discretized node (node ​​1) is given by the value of d. –1 ); h atm Soil water head (cm) in equilibrium with relative humidity of air; h 1 represents the soil water head (cm) at the first node; Z 1 represents the soil depth (cm) of the first node.

[0079]

[0080] In the formula, β It is a soil-specific parameter (mm d) characterizing the evaporation process. –1 / 2 ); t dry It is the time (d) after the heavy rain.

[0081] Soil evaporation reduction rate under plastic film mulching conditions ( C film ) and coating ratio ( δ m The relationship between them is:

[0082] When the leaf area index is less than 1 cm 2 cm –2 When LAI < 1, there is a significant correlation between the daily bare soil surface temperature and the daily air temperature, the previous day's air temperature, and the previous day's bare soil surface temperature:

[0083] In the formula, T top The temperature of the bare soil surface on that day (°C). T top′ The temperature of the bare soil surface (°C) was the previous day. T air The temperature of the day (°C); T air ′ represents the temperature (°C) of the previous day.

[0084] When the leaf area index is greater than 1 cm 2 cm –2 When LAI > 1, the soil surface temperature under uncovered conditions is similar to... T top ′ ,T air It is related to LAI, specifically:

[0085] Soil surface temperature under mulching conditions T ftop The soil temperature is determined by the average daily air temperature, the maximum daily temperature, the film thickness, and the film thermal conductivity. These are used as the upper boundary conditions for soil temperature, and the specific calculation method is as follows:

[0086]

[0087] In the formula, dH The heat flow (J) from the film surface to the soil surface; T max The highest daily temperature (°C); T ftop The soil surface temperature after mulching (°C); D film Thin film thickness (cm); TC film Thin film thermal conductivity (J cm) –1 ℃ –1 d –1 ); t The unit of time is (d). C soil Soil specific heat capacity (J) cm –3 ℃ –1 ); dV Set to 1 cm 3 .

[0088] Soil temperature boundary conditions are estimated using a general method at the lower boundary. The specific method is as follows:

[0089]

[0090] In the formula, T bot The soil temperature (°C) at depth Z of the lower boundary soil layer. T mean The average annual temperature (°C); T ampl The fluctuation range of air temperature (°C); d demp Damping depth (cm); JD Ordinal number of the year; JD 0 represents the year number at which the air temperature reaches its maximum; λ a The average thermal conductivity of the soil profile (J cm⁻¹) 1 C 1 d –1 ); C a The average heat capacity of the soil profile (J / cm²) 3 C 1 ).

[0091] The rate of crop growth and development is controlled by the temperature of the plant's meristem, which is located underground during the early stages of growth and development. In this application, soil temperature at a depth of 5 cm is used to simulate different stages of crop development. D s—film The specific calculation formula is as follows:

[0092]

[0093] In the formula, D s—film ( JD +1) and D s—film ( JD The year numbers are respectively JD +1 and JD Crop growth and development stages under mulching conditions; T eff—film The effective temperature (°C) before the six-leaf stage under plastic film mulching. T soil5 The soil temperature (°C) at a depth of 5 cm. T sum，i The temperature (°C) required to complete the vegetative or reproductive period; T b Base point temperature (°C); T 0 represents the optimal temperature (°C).

[0094] Step 3: Input crop growth, soil hydrothermal data, and meteorological data into the SWAP crop model to calibrate and validate the soil hydraulic parameters, soil thermal parameters, and crop parameters in the model. A trial-and-error method is used to narrow down the parameter range. Experimental data from 2017 were used to calibrate the model parameters, while experimental data from 2018 and 2019 were used to validate the model parameters. During the calibration process, soil hydraulic parameters were first calibrated based on dynamic changes in soil moisture from the soil profile; then, soil thermal parameters were calibrated based on changes in soil temperature from the soil profile; and finally, crop parameters were calibrated based on maize leaf area index, dry matter accumulation, and yield. The normalized root mean square error (NRMSE) and coefficient of determination (R²) were used to calibrate the parameters. 2 To evaluate model performance.

[0095] Step 4: The application of the SWAP model under different irrigation regimes in this embodiment is the same as in Embodiment 2, and will not be repeated here.

[0096] Example 4 like Figure 4 The diagram shown is a structural diagram of the CropSPAC model in this embodiment. The solution in this embodiment is as follows: Step 1: Verify that the methods for obtaining the crop data, soil data, meteorological data and other indicators required for the crop model in this embodiment are the same as those in Embodiments 2 and 3, and will not be repeated here.

[0097] Step 2: In this embodiment, the method for considering the mulch film covering factor in the crop model is as follows: In this embodiment, the farmland water and heat transport module is a comprehensive water and heat migration model that combines soil hydrodynamics, micrometeorology, and energy balance principles to describe the soil-vegetation-atmosphere continuum, namely the SPAC water and heat transport model. The SPAC water and heat transport model consists of energy balance, distribution, and water and heat transfer processes above the surface, and soil water and heat migration processes below the surface. The former provides the upper boundary conditions for the transport of soil moisture (soil evaporation) and heat (soil heat flux) for the latter. Taking the CropSPAC model as an example... The entire agricultural ecosystem is divided into three layers: the soil-mulch layer, the crop canopy layer, and the atmosphere. The total net radiation flux is:

[0098] In the formula, R n Total net radiative flux (W m) –2 ); R v Net radiation flux intercepted by crop canopy (W m) –2 ); R s–m The net radiation flux reaching the soil-mulch system (W m)–2 ).

[0099] R n It is related to the atmosphere, crop canopy, mulch film, land surface conditions, and longwave and shortwave radiation fluxes. The calculation method is as follows:

[0100] In the formula, R s Solar radiation flux (W m) –2 ); R la , R lp , R ls and R lm Longwave radiation flux (W / m²) emitted by the atmosphere, crop canopy, soil surface, and plastic mulch film, respectively. –2 ); ρ 1 and ρ 2 are respectively covered with film δ m Partial and Uncoated 1– δ m The reflectivity of a portion of the underlying surface; τ lp and τ lm The values ​​represent the long-wave radiation transmittance of the crop canopy and the plastic mulch film, respectively. ρ 1. ρ 2 and τ lp Related to LAI, the calculation method is as follows:

[0101]

[0102]

[0103]

[0104] In the formula, ρ p , ρ s and ρ m The shortwave radiation reflectance of the crop canopy, soil surface, and plastic mulch film are respectively. CC The crop canopy coverage rate (%) is given; LAI is calculated using the following formula:

[0105]

[0106] In the formula, LAI i Leaf area index (cm²) of the previous day 2 cm –2 ); RD Δ represents the leaf senescence rate. LAI leaf area index increment (cm) 2 cm –2 ); Δ W leaf Leaf weight increment per unit land area (kg cm) –2 ); SLA Specific leaf area (cm²) 2 kg –1 ), which is related to the cumulative effective temperature.

[0107] R v Calculations were performed using empirical methods:

[0108] R s–m Including the net radiative flux reaching the plastic mulch film ( R nm ) and the net radiation flux reaching the soil (R ns ),Right now

[0109] R ns The calculation method is as follows:

[0110]

[0111] In the formula, τ sp and τ sm The values ​​represent the shortwave radiation transmittance of the crop canopy and the plastic mulch film, respectively.

[0112] In the CropSMPAC model, the energy consumed by crop photosynthesis and the water and heat stored in the canopy air are neglected. R v and R ns The following formula represents:

[0113]

[0114]

[0115]

[0116] In the formula, LE v and LE s-m The latent heat fluxes of leaf transpiration and soil surface evaporation (W m) are respectively. –2 ); H v and H s-m These represent the sensible heat fluxes (W / m³) between crop leaves and canopy air, and between the soil surface and canopy air, respectively. –2 ); G Soil heat flux (W m) –2 ); H and LE These represent the sensible and latent heat fluxes between the canopy air and the atmosphere at reference altitude (W / m²). –2 ).

[0117] Based on the principles of aerodynamics and micrometeorology, the water-heat exchange process above the Earth's surface is represented by the following equation:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] in ρ , C p and γ air density (kg m³) 3 ), specific heat capacity at constant pressure (J kg) 1 K 1 ) and hygrometer constant (mbar K) 1 ); e a and T a These are the air vapor pressure (mbar) and temperature (°C) at the reference altitude, respectively. e b and T bThese are the water vapor pressure (mbar) and temperature (°C) of the canopy air, respectively. e v and T v These are the saturated vapor pressure (mbar) and temperature (°C) of the blade, respectively. e s and T s These are the water vapor pressure (mbar) and temperature (°C) at the soil surface, respectively. r ba Aerodynamic drag (Sm) for water and heat transfer from the canopy to the atmosphere 1 ); r vb Canopy boundary layer drag (Sm) is the force of water and heat transfer from the blades to the canopy. 1 ); r sb Aerodynamic drag (Sm) for water and heat transfer from the soil surface to the canopy 1 ); r v Total stomatal resistance of the canopy (Sm 1 ); r s Evaporation resistance of soil surface (sm 1 ); r sm Thermal contact resistance (sm 1 ), expressed by the following formula:

[0124] In the formula, Z sm The distance between the soil surface and the plastic film (m); K Molecular diffusion coefficient (W m) 1 °C 1 ); N u The Nusselt number is used to reflect heat conduction.

[0125] The crop growth and development stage is determined by the cumulative growth days. The daily growth days (GDD) are calculated using the following formula:

[0126] in T 0 represents the optimal temperature (°C). T bReference temperature (°C); T soil5,max and T soil5,min The soil temperatures (°C) at a depth of 5 cm are the highest and lowest temperatures of the day, respectively.

[0127] when T soil5,max and T soil5,min When the value is outside the above range, a temperature change factor is introduced to calculate the hourly growth degree-day, and then the hourly growth degree-day is accumulated using the following formula to obtain the daily growth degree-day.

[0128]

[0129]

[0130]

[0131] In the formula, Tfac ( i = 1, 2, 3, ..., 24) represents the temperature variation factor for each time interval; T ( i ) represents its corresponding temperature; T m This represents the highest temperature during the crop's growth stage.

[0132] Step 3: Input crop growth, soil hydrothermal data, and meteorological data into the CropSPAC crop model. Calibrate and validate the parameters involved in the energy budget and distribution process, soil hydraulic parameters, soil thermal parameters, and crop parameters in the model, using a trial-and-error method to narrow down the parameter range. Experimental data from 2017 were used to calibrate the model parameters, while experimental data from 2018 and 2019 were used to validate them. During the calibration process, firstly, the parameters involved in the energy budget and distribution process, soil hydraulic parameters, and soil thermal parameters were calibrated based on the dynamic changes in soil moisture and soil temperature in the soil profile. Then, crop parameters were calibrated based on the leaf area index, dry matter accumulation, and yield of maize. The normalized root mean square error (NRMSE) and coefficient of determination (R²) were used to calibrate the parameters. 2 To evaluate model performance.

[0133] Step 4: The application of the CropSPAC model under different irrigation regimes in this embodiment is the same as in Embodiments 2 and 3, and will not be repeated here.

[0134] Simulation results of soil water storage and consumption ( Figure 5 a in Figure 5Figure b) shows that the AquaCrop, SWAP, and CropSPAC models can all simulate the water balance components of mulched maize fields well. The R² and NRMSE for soil water storage simulation were 0.74–0.78 and 9.53%–11.02%, respectively, while the R² and NRMSE for water consumption simulation were 0.83–0.95 and 7.20%–11.20%, respectively. The simulation results for soil temperature (…) Figure 5 c) indicates that both the SWAP and CropSPAC models can simulate soil temperature well, with R² values ​​above 0.80 and NRMSE values ​​ranging from 5.05% to 5.65%. Simulation results for canopy cover, dry matter accumulation, and yield (…) Figure 5 d in Figure 5 e in Figure 5 f) indicates that the AquaCrop, SWAP, and CropSPAC models can all simulate crop growth processes well. The R² values ​​between the simulated canopy cover, dry matter accumulation, and yield and the measured values ​​of the three models are all higher than 0.93, with NRMSE values ​​of 7.52%–8.84%, 13.86%–15.53%, and 4.31%–7.59%, respectively.

[0135] Figure 6 Figures (a, b, c, d) show that the simulated trends of soil water storage, canopy cover, dry matter accumulation, and soil temperature under different irrigation treatments (WF, WM, and WL) are consistent with the measured values. All three models can simulate soil water storage, canopy cover, dry matter accumulation, and soil temperature under the WF treatment well. Figure 7The results show that the NRMSE and R² of the three models simulating soil water storage are 6.01%–8.38% and 0.59–0.67 under the WF treatment, 5.93%–11.63% and 0.50–0.63 under the WM treatment, and 16.98%–20.92% and 0.54–0.57 under the WL treatment, indicating that the three models simulate soil water storage better under the WF treatment than under the WM and WL treatments. Under the WF, WM, and WL treatments, the R² of the simulated canopy cover of the three models is close to 1 compared to the measured values. The NRMSE of the AquaCrop and CropSPAC models simulating canopy cover is lower under the WF treatment than under the WM and WL treatments, indicating that these two models simulate canopy cover better under the WF treatment than under the WM and WL treatments; the SWAP model shows the opposite. Under WF and WL treatments, the AquaCrop model performed better than the other two models in simulating canopy cover, while the opposite was true under the WM treatment. Except for the dry matter accumulation of the SWAP model under the WM treatment, the NRMSE of the three models simulating dry matter accumulation was lower under the WF treatment (10.58%–13.67%) than under the WM (15.04%–16.62%) and WL (18.33%–21.20%) treatments, indicating that the three models performed better in simulating dry matter accumulation under the WF treatment than under the WM and WL treatments.

[0136] Figure 6 (e) and Figure 7 The results show that, except for the output of the CropSPAC model under the WM treatment, the NRMSE of the output simulation of the three models under the WF treatment (2.05%~2.59%) is lower than that under the WM treatment (6.43%~11.26%) and the WL treatment (12.28%~15.57%), indicating that the three models under the WF treatment are better at simulating output than those under the WM and WL treatments.

[0137] Figure 6 (f) and Figure 7 It also shows that, except for the water consumption of the SWAP model under the WM treatment, the NRMSE of the simulated water consumption of the three models is lower under the WF treatment than under the WM and WL treatments, indicating that the simulation effect of the three models on water consumption is better under the WF treatment than under the WM and WL treatments. Under the WF and WL treatments, the CropSPAC model simulates water consumption better than the other two models, while the opposite is true under the WM treatment.

[0138] Figure 8 The results show that for the AquaCrop, SWAP, and CropSPAC models, crop yield and water productivity exhibit a significant quadratic parabolic relationship with irrigation interval and irrigation amount, with R² values ​​all exceeding 0.671, and most exceeding 0.802. Within the irrigation interval range specified in this application, water productivity increases with increasing irrigation interval. Figure 8 a- Figure 8 (c) For the AquaCrop model, yield decreases with increasing irrigation interval; while for the SWAP and CropSPAC models, yield initially increases and then decreases with increasing irrigation interval. For the three models, the overall score calculated using the entropy weight method shows that the highest overall score is achieved when the irrigation interval is 14 days, with AquaCrop, SWAP, and CropSPAC models reaching 0.830, 0.913, and 0.916, respectively (Table 1).

[0139] Simulation results from the AquaCrop and CropSPAC models show that within the irrigation range specified in this application, maize yield increases with increasing irrigation volume; while the SWAP model shows a trend of first increasing and then decreasing, with the highest yield corresponding to an irrigation volume of 345.0 mm. Figure 8 d- Figure 8 (f) Within the irrigation range specified in this application, the water productivity of the CropSPAC model decreases with increasing irrigation volume; the water productivity of the AquaCrop and SWAP models shows a trend of first increasing and then decreasing, with the highest water productivity corresponding to irrigation volumes of 100.0 mm and 335.7 mm, respectively. For the AquaCrop model, the irrigation volume corresponding to the highest overall score is 80% WF, and the second and third highest overall scores correspond to 90% WF and 100% WF, respectively (Table 1). For the SWAP model, the irrigation volume corresponding to the highest overall score is 110% WF, and the second and third highest overall scores correspond to 120% WF and 100% WF, respectively. For the CropSPAC model, the irrigation volume corresponding to the highest overall score is 100% WF, and the second and third highest overall scores correspond to 70% WF and 90% WF, respectively. Overall, 100% WF is the optimal irrigation volume recommended by all three models.

[0140] Table 1

[0141] In summary, compared with the prior art, this application has at least the following beneficial effects: 1) This application comprehensively considers the impact of plastic film mulching on rainfall interception, soil evaporation, soil temperature and crop growth and development stages, breaking through the single and incomplete consideration of the mulching effect in existing models, and enabling the model to more comprehensively reflect the farmland ecological effects of mulching.

[0142] 2) This application proposes a method for directly introducing the effect of plastic film mulching, which enables the model to simulate soil water and heat transport and crop growth under mulching conditions based on physical mechanisms, thereby improving the realism of the simulation results and the accuracy of prediction.

[0143] Based on the principle and structure of each model, this application embodiment has formulated a targeted scheme for introducing mulching factors, which ensures that each model can reasonably and accurately simulate the key processes of soil water and heat transfer and crop growth under mulching conditions within its own framework.

[0144] Example 5 In yet another embodiment, such as Figure 9 As shown, this application provides a simulation system 10 that considers the mulch film covering factor in a crop growth model, comprising: The raw data acquisition module 100 is used to acquire crop growth data, soil hydrothermal data and meteorological data under different irrigation conditions in the study area; The growth model construction module 200 is used to introduce the corresponding mulch film mulching module into the crop growth model based on the effects of mulch film mulching on rainfall interception, soil evaporation, soil temperature and crop growth and development stages, and to construct a crop growth model under mulch film mulching. The parameter calibration and verification module 300 is used to input the acquired crop growth data, soil water and heat data and meteorological data into the crop growth model under the mulch film, and to calibrate and verify the soil hydraulic parameters, thermal parameters and crop parameters in the model so that the simulated values ​​match the measured values. The simulation output module 400 is used to simulate various irrigation regimes based on the actual meteorological conditions and soil properties of the study area and the validated crop growth model, and to determine the irrigation regulation mode of the mulched farmland based on the simulation results.

[0145] This application provides a simulation system that considers plastic film mulching factors in a crop growth model. This system can accurately simulate the crop growth process and soil hydrothermal dynamics under plastic film mulching, providing a scientific tool for the subsequent application of crop models in plastic film-mulched farmland.

[0146] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0147] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A simulation method that considers the effect of plastic film mulching in a crop growth model, characterized in that, Includes the following steps: Acquire crop growth data, soil hydrothermal data, and meteorological data under different irrigation conditions in the study area; Based on the effects of plastic film mulching on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, a corresponding plastic film mulching module is introduced into the crop growth model to construct a crop growth model under plastic film mulching. The acquired crop growth data, soil water and heat data, and meteorological data are input into the crop growth model under the mulch film. The soil hydraulic parameters, thermal parameters, and crop parameters in the model are calibrated and verified to ensure that the simulated values ​​match the measured values. Based on the actual meteorological conditions and soil properties of the study area, a variety of irrigation regimes were simulated using a validated crop growth model, and the irrigation regulation mode for farmland covered with plastic film was determined based on the simulation results.

2. The method as described in claim 1, characterized in that, The process of constructing a crop growth model under plastic film mulch, based on the effects of plastic film mulch on rainfall interception, soil evaporation, soil temperature, and crop growth and development stages, involves introducing a corresponding plastic film mulch module into the crop growth model. Based on the interception effect of mulch on rainfall, the film interception process is quantified in a crop growth model to calculate the actual amount of rainfall infiltrating into the soil. Based on the inhibitory effect of plastic film mulching on soil evaporation, a soil evaporation reduction factor was introduced into the crop growth model to calculate the amount of soil evaporation under mulching conditions. Based on the warming effect of plastic film mulching on soil temperature, plastic film parameters related to soil heat are introduced into the crop growth model to calculate the soil surface temperature under mulching conditions. Based on the promoting effect of soil warming under plastic film mulching on crop growth and development stages, the relationship between crop development stages and soil temperature is established in a crop growth model to calculate the crop growth and development stages under mulching conditions.

3. The method as described in claim 2, characterized in that, The quantitative film retention process, used to calculate the actual amount of rainfall infiltrating into the soil, includes: Canopy interception is calculated based on leaf area index and soil cover ratio; The film retention amount is calculated based on the difference between the total rainfall and the canopy retention amount; The actual amount of rainfall that infiltrates into the soil is calculated based on total rainfall, canopy interception, and film interception.

4. The method as described in claim 2, characterized in that, The introduction of a soil evaporation reduction factor to calculate soil evaporation under mulching conditions includes: calculating soil evaporation under mulching conditions based on mulching ratio and mulching resistance.

5. The method as described in claim 2, characterized in that, The method of introducing soil heat-related mulch film parameters to calculate the soil surface temperature under mulch film conditions includes: calculating the soil surface temperature under mulch film conditions by considering the influence of film thickness and film thermal conductivity on the heat transfer between the film and the soil surface; or calculating the soil temperature under mulch film conditions by considering the influence of mulch film control factors, including mulch film optical property parameters, mulch film ratio, and film-soil spacing on the surface energy balance budget and distribution process.

6. The method as described in claim 2, characterized in that, The process of establishing the relationship between crop development stages and soil temperature to calculate crop growth and development stages under mulching conditions includes: The crop growth and development stages under mulching conditions are calculated based on the soil temperature at a depth of 5cm, the baseline temperature, and the temperature required to complete the nutrient or reproductive stages.

7. The method as described in claim 1, characterized in that, The calibration and validation of the soil hydraulic parameters, thermal parameters, and crop parameters in the model include: Based on the obtained crop growth data, soil water and heat data, and meteorological data of the study area, the soil hydraulic parameters were first calibrated based on the dynamic changes in soil moisture in the soil profile, then the soil thermal parameters were calibrated based on the temperature changes in the soil profile, and finally the crop parameters were calibrated based on the crop leaf area index, dry matter accumulation, and yield. The model parameters were then validated using the remaining experimental data.

8. The method as described in claim 1, characterized in that, The simulation of multiple irrigation regimes includes setting different irrigation intervals and irrigation volume scenarios, and using the validated crop growth model to simulate crop growth and soil hydrothermal conditions under each scenario.

9. The method as described in claim 8, characterized in that, The method for determining the irrigation regulation mode of farmland covered with plastic film based on simulation results includes: using the entropy weight method to comprehensively evaluate different irrigation systems, and determining the optimal irrigation system based on the comprehensive score.

10. A simulation system that considers the effect of plastic film mulching in a crop growth model, characterized in that, include: The raw data acquisition module is used to acquire crop growth data, soil hydrothermal data, and meteorological data under different irrigation conditions in the study area; The growth model construction module is used to introduce the corresponding mulch film module into the crop growth model based on the effects of mulch film on rainfall interception, soil evaporation, soil temperature and crop growth and development stages, and to construct the crop growth model under mulch film. The parameter calibration and verification module is used to input the acquired crop growth data, soil water and heat data and meteorological data into the crop growth model under the mulch film, and to calibrate and verify the soil hydraulic parameters, thermal parameters and crop parameters in the model so that the simulated values ​​match the measured values. The simulation output module is used to simulate various irrigation regimes based on the actual meteorological conditions and soil properties of the study area, using a validated crop growth model, and to determine the irrigation regulation mode for farmland covered with plastic film based on the simulation results.

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